| Author | Silfiani, Mega |
| Note | A thesis submitted in partial fulfillment of the requirements for the
degree of Master of Science
Industrial and Manufacturing Engineering, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Abstract | Hybrid autoregressive integrated moving average (ARIMA) and artificial neural networks
(ANNs) ensemble is methodology which generating multi-model of hybrid ARIMA and
ANNs then combining the output of each hybrid into single output. Meanwhile hybrid
ARIMA and ANNs model is combination of ARIMA and ANNs which residual of
ARIMA is used to input of ANNs. Hybrid ARIMA and ANNs ensemble are constructed to
improve the performance of ARIMA for forecasting inflation in Indonesia. The
architechture of ANNs in this research are feedforward neural network (FFNNs), recurrent
neural network (RNNs), radial basis function neural network (RBFNNs) and generalized
regression neural network (GRNNs) This research evaluates performance of hybrid
ARIMA and ANNs ensemble based on root mean square error (RMSE), relative root mean
square error (RelRMSE) and log mean square error ratio (LMR). This reserach use national
inflation and seven cities in East Java as case study. The result show that, in the context of
forecasting inflation, hybrid ARIMA and ANNs ensemble is better than ARIMA,
particularly when the stacking hybrid ARIMA and GRNNs is used. Overall, stacking
technique is better than averaging to combine ensemble member of hybrid ARIMA and
ANNs. |
| Year | 2014 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Industrial Systems Engineering (ISE) |
| Chairperson(s) | Huynh Trung Luong; |
| Examination Committee(s) | Bohez, Erik L.J. ;Parnichkun, Manukid ; |
| Scholarship Donor(s) | Directorate General of Higher Education
(DIKTI), Indonesia; |
| Degree | Thesis (M.Eng.) -- Asian Institute of Technology, 2014 |